AI app for it and development · no coding needed
Cross-project assistant memory and review console
Reduce repeated briefing and context mixing while keeping memory inspectable and correctable.
Made for: Teams and developers running AI assistants across several projects and conversations
What it does for you
The problem
Assistant context is lost between sessions and mixed across projects, so users repeat instructions and cannot see or correct what was saved.
What it gives you
Searchable, editable memory library with review schedules and progress analytics
What you give it
Conversation historyuploaded documentsproject instructionslocal files
Build your own version of Claude Memory, Claude Artifacts and more
One app with what these 5 AI tools do, yours to keep and change: Claude Memory, Claude Artifacts, Claude Cowork Projects, Zep, Rememberall.
Everything these tools do, in one app
- Persistent conversation memory Stores conversation context so the assistant can recall it in later sessions.Found in Claude Memory, Claude Cowork Projects, Zep
- Project-scoped context Keeps memory and instructions separated by project to avoid mixing workstreams.Found in Claude Memory, Claude Artifacts, Claude Cowork Projects
- View and edit memories Lets users see and change what the assistant has saved.Found in Claude Memory
- Disable saved memories Allows users to turn off memory retention.Found in Claude Memory
- Incognito chats Provides chats that are not stored.Found in Claude Memory
- Shared team context Shares stored context across collaborators in a team.Found in Claude Memory, Claude Artifacts
- Document upload Lets users add documents to enrich AI interactions.Found in Claude Artifacts
- Custom project instructions Sets per-project instructions to refine AI responses.Found in Claude Artifacts, Claude Cowork Projects
- Centralized knowledge and chat Combines internal knowledge and chat activity in one place.Found in Claude Artifacts
- Local file context Uses folders and local files alongside related tasks.Found in Claude Cowork Projects
- Scheduled recurring tasks Automates tasks on a schedule inside a project.Found in Claude Cowork Projects
- Persistent project workspace Combines tasks, files, and notes in one workspace.Found in Claude Cowork Projects
- Desktop environment integration Works inside the Claude Desktop environment.Found in Claude Cowork Projects
- Continuous learning Updates the assistant from ongoing user interactions.Found in Zep
- Dialog intent classification Classifies the intent of conversations to organize context.Found in Zep
- Temporal knowledge graph Integrates business data and chat messages into a knowledge graph for accurate user facts.Found in Zep
- Scalable secure data management Supports large numbers of users and facts with compliance and privacy controls.Found in Zep
- Spaced repetition schedules Adjusts review timing based on user performance.Found in Rememberall
- Smart review notifications Prompts timely reviews to reinforce learning.Found in Rememberall
- Multi-format content support Accepts text, images, and audio notes.Found in Rememberall
- Progress analytics Tracks retention over time with detailed analytics.Found in Rememberall
- Note-taking app integrations Connects with popular note-taking and productivity apps.Found in Rememberall
How it works, step by step
- Store conversation context for later sessions
- Separate memory and instructions by project
- View and edit saved memories
- Disable memory retention per chat or project
- Provide incognito chats that are not stored
- Share stored context across team collaborators
- Upload documents to enrich interactions
- Set custom per-project instructions
- Combine internal knowledge and chat activity in one place
- Use local folders and files alongside related tasks
- Run scheduled recurring tasks inside a project
- Keep tasks, files and notes in one persistent workspace
- Integrate with the desktop environment
- Update the assistant from ongoing interactions
- Classify dialog intent to organize context
- Build a temporal knowledge graph of business data and messages
- Support many users and facts with compliance and privacy controls
- Adjust review timing based on performance
- Send timely review notifications
- Accept text, images and audio notes
- Track retention over time with analytics
- Connect with note-taking and productivity apps
Build it yourself with your AI system
Build this app yourself, no coding needed
Start with a quick version you can try in a few minutes. Like it? Then build the full app by copying and pasting our step-by-step instructions: everything is prepared for you.
Sign in to see how to build it yourself
Build a quick version to try, or get the full app pack for Cross-project assistant memory and review console with the step-by-step building instructions. You don't need any technical skills: you copy, paste and answer a few questions. Both are included in the membership.
4 Have it built for you days to a few weeks
Rather not do it yourself, or want it fully tailored to your data, your way of working and your brand? Nexibeo builds Cross-project assistant memory and review console with you.
What's in the app pack
Included in the Complete AI Training membership.
- The building instructions your AI follows, step by step
- The questions your AI will ask you about your business before it starts
- A clickable demo you can open in your browser, to see how it should work
- A detailed blueprint of the screens, the information it keeps and the checks it runs
Become a member to get the app packAlready a member? Sign in
The files, for the technically curious
- START-HERE.mdHow to build it with your own AI (read first)3 KB
- README.mdOverview and links4 KB
- questions.mdQuestions to answer before you build3 KB
- prompt-cloudflare.mdThe full build prompt, hosted on Cloudflare24 KB
- prompt-vps.mdThe same build on your own server (Docker)24 KB
- spec.jsonData model, API, AI pipeline, acceptance criteria12 KB
- demo/index.htmlThe working demo on sample data196 KB
Questions
Do I need to know how to code?
No. You copy and paste the prompts on this page into ChatGPT or Claude, and the AI does the building. When it asks you something, you answer in your own words.
What does it cost?
The quick version, the app pack and the step-by-step instructions are for members: you pay the membership price, not a price per app (see the plans). Building the full app uses your own ChatGPT or Claude subscription. Putting it online is often cheap or no cost at the start, and your AI tells you before anything costs money.
How long does it take?
The quick version: about two minutes. The real app: an afternoon for a first version you can use, longer if you want every feature.
Can I change it to fit my business?
Yes. Tell your AI what to change in plain words, like “add a column for the price” or “use our logo and colours”. Or have Nexibeo build and customise it for you.
More detailsHow the AI works, safeguards and what to build first
Reduce repeated briefing and context mixing while keeping memory inspectable and correctable. For teams and developers running AI assistants across several projects and conversations, convert conversation history, uploaded documents, project instructions and local files into a searchable, editable memory library with review schedules and progress analytics. The benefit is a testable hypothesis, measured through repeated briefing time avoided per project and memory corrections per review cycle; do not assume that AI output alone produces business value.
Confirm the buyer's problem and scope, collect conversation history, uploaded documents, project instructions and local files, then follow this sequence: 1. Store conversation context for later sessions. 2. Separate memory and instructions by project. 3. View and edit saved memories. Resolve uncertain cases with qualified reviewers, approve searchable, editable memory library with review schedules and progress analytics, and measure repeated briefing time avoided per project and memory corrections per review cycle against a documented baseline.
How the AI works
Use AI to interpret permitted inputs, suggest structured mappings and generate candidate outputs for the three stated task modules. Use deterministic code for arithmetic, schema validation, hard constraints and reproducible tests. Review source-linked explanations and uncertainty before accepting results. One assistant provider and one desktop environment; final memory approval and data classification remain human. A model suggestion is never a verified fact, professional decision or authorization to act.
Safeguards
Preserve data ownership, source attribution, consent and usage permissions. Users approve substantive memory changes and sharing scope. One assistant provider and one desktop environment; final memory approval and data classification remain human. Keep all consequential actions under authorized human control and do not fabricate missing inputs, permissions, professional judgments or market evidence.
What to build first
Pilot scope: One assistant provider and one desktop environment; final memory approval and data classification remain human. Implement one approved input format, a bounded representative case set and the first two task modules: store conversation context for later sessions; separate memory and instructions by project. Support the third module with operator review: view and edit saved memories. Include source references, corrections, basic organization access, approval states, export and value measurement. Use managed operator assistance for unresolved exceptions. The cost estimate covers this narrow prototype, not unrestricted multi-tenant scale, complex production integrations, specialist certification or physical operations.
What it can connect to
Assistant provider APIs, desktop environment, note-taking and productivity apps, local file folders and cloud storage. Start with file exchange and validate destination specifications before promising direct synchronization. Start with authorized file exchange. Validate current provider access, usage rights and schema behavior before promising a connector.
The screens in detail
Primary screens: Memory library and search, Project workspace and instructions, Review and analytics. Use a searchable list of stored memories with filters by project, source and date, a detail panel for editing or disabling a memory, and a project workspace combining tasks, files and notes. Let users compare a memory against its source conversation or document. Display saved, edited, disabled and pending-review states. Provide a team view of shared context with per-member permissions. Make the task-specific outcome searchable, editable memory library with review schedules and progress analytics visible beside its evidence, review state and value baseline.
